RT Journal Article
SR Electronic
T1 Accurate eQTL prioritization with an ensemble-based framework
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 069757
DO 10.1101/069757
A1 Haoyang Zeng
A1 Matthew D. Edwards
A1 Yuchun Guo
A1 David K. Gifford
YR 2016
UL http://biorxiv.org/content/early/2016/08/16/069757.abstract
AB Expression quantitative trait loci (eQTL) analysis links sequence variants with gene expression change and serves as a successful approach to fine-map variants causal for complex traits and understand their pathogenesis. In this work, we present an ensemble-based computational framework, EnsembleExpr, for eQTL prioritization. When trained on data from massively parallel reporter assays (MPRA), EnsembleExpr accurately predicts reporter expression levels from DNA sequence and identifies sequence variants that exhibit significant allele-specific reporter expression. This framework achieved the best performance in the “eQTL-causal SNPs” open challenge in the Fourth Critical Assessment of Genome Interpretation (CAGI 4). We envision EnsembleExpr to be a powerful resource for interpreting non-coding regulatory variants and prioritizing disease-associated mutations for downstream validation.